Upload decisions_users.py

utils/decisions_users.py

@@ -176,48 +176,48 @@ def visualize_bar_plot(df_data):
     fig = px.bar(df_data, x="probability", y="class", orientation='h')
     return fig
 
-def run_instance_exp(img_path, img_path_list,prob_list,grad_vis_path_list):
-
-    st.subheader('Instance Exploration')
-    # st.columns((1,1,1))  with row4_2:
-    LABELS = ['CNV', 'DRUSEN', 'DME', 'NORMAL']
-
-    left_column, middle_column, right_column = st.columns((1,1,1))
-    display_image = load_image(img_path)
-    # fig = px.imshow(display_image)
-    # left_column.plotly_chart(fig, use_container_width=True)
-    left_column.image(cv2.resize(display_image, (180,180)),caption = "Selected Input")
-
-    # get class probabilities
-    indx = img_path_list.index(img_path)
-    print(img_path)
-    prb_tmp = prob_list[indx]
-    print(f"{prb_tmp[1:-1]}")
-    clss_probs = literal_eval('"'+prb_tmp[1:-1]+'"')
-    print(clss_probs[1:-1].split(' '))
-    prob_cls = [float(p) for p in clss_probs[1:-1].split(' ')]
-    tmp_df = pd.DataFrame.from_dict({'class':LABELS,'probability':prob_cls})
-    print(tmp_df.head())
-    fig = plt.figure(figsize=(15, 13))
-    sns.barplot(x='probability', y='class', data=tmp_df)
-    middle_column.pyplot(fig)
-    # st.caption('Predictions')
-
-    tmp_grad_img = GRAD_CAM_IMAGE_DIR + img_path.split("\\")[-2] +'/'+img_path.split("\\") [-1] 
+# def run_instance_exp(img_path, img_path_list,prob_list,grad_vis_path_list):
+
+#     st.subheader('Instance Exploration')
+#     # st.columns((1,1,1))  with row4_2:
+#     LABELS = ['CNV', 'DRUSEN', 'DME', 'NORMAL']
+
+#     left_column, middle_column, right_column = st.columns((1,1,1))
+#     display_image = load_image(img_path)
+#     # fig = px.imshow(display_image)
+#     # left_column.plotly_chart(fig, use_container_width=True)
+#     left_column.image(cv2.resize(display_image, (180,180)),caption = "Selected Input")
+
+#     # get class probabilities
+#     indx = img_path_list.index(img_path)
+#     print(img_path)
+#     prb_tmp = prob_list[indx]
+#     print(f"{prb_tmp[1:-1]}")
+#     clss_probs = literal_eval('"'+prb_tmp[1:-1]+'"')
+#     print(clss_probs[1:-1].split(' '))
+#     prob_cls = [float(p) for p in clss_probs[1:-1].split(' ')]
+#     tmp_df = pd.DataFrame.from_dict({'class':LABELS,'probability':prob_cls})
+#     print(tmp_df.head())
+#     fig = plt.figure(figsize=(15, 13))
+#     sns.barplot(x='probability', y='class', data=tmp_df)
+#     middle_column.pyplot(fig)
+#     # st.caption('Predictions')
+
+#     tmp_grad_img = GRAD_CAM_IMAGE_DIR + img_path.split("\\")[-2] +'/'+img_path.split("\\") [-1] 
     
-    display_image = load_image(tmp_grad_img,replace=False)
-    # left_column.plotly_chart(fig, use_container_width=True)
-    right_column.image(display_image,caption = "ROI")
+#     display_image = load_image(tmp_grad_img,replace=False)
+#     # left_column.plotly_chart(fig, use_container_width=True)
+#     right_column.image(display_image,caption = "ROI")
 
-    # seed_id = 900
-    seed_id = test_id_list.index(img_path)
-    query_item = test_features[seed_id]
-    print(query_item.shape)
-    closest_idxs = annoy_matching(annoy_tree,query_item, seed_id, 10)
-    closest_fns = [train_ids[close_i] for close_i in closest_idxs]
-    st.subheader('Top-10 Similar Samples from Gallery Set')
-    st.plotly_chart(plot_n_similar(seed_id,closest_fns, img_path,n=10, scale=4), use_container_width=True)
-    # st.pyplot(plot_n_similar(seed_id,closest_fns, img_path,n=10, scale=4))
+#     # seed_id = 900
+#     seed_id = test_id_list.index(img_path)
+#     query_item = test_features[seed_id]
+#     print(query_item.shape)
+#     closest_idxs = annoy_matching(annoy_tree,query_item, seed_id, 10)
+#     closest_fns = [train_ids[close_i] for close_i in closest_idxs]
+#     st.subheader('Top-10 Similar Samples from Gallery Set')
+#     st.plotly_chart(plot_n_similar(seed_id,closest_fns, img_path,n=10, scale=4), use_container_width=True)
+#     # st.pyplot(plot_n_similar(seed_id,closest_fns, img_path,n=10, scale=4))
 
 
 
@@ -254,8 +254,8 @@ def run_instance_exp_keras_model(img_path, new_model, feature_extractor_model):
     # middle_column.write("Probabilities")
 
     # grad img 
-    # right_column.image(roi_img, caption = "Decision ROI")
-    right_column.image(cv2.resize(roi_img, display_image.shape[:2]),caption = "GradCAM ROI")
+    right_column.image(roi_img, caption = "Decision ROI")
+    # right_column.image(cv2.resize(roi_img, display_image.shape[:2]),caption = "GradCAM ROI")
 
     # seed_id = 900
     seed_id = test_id_list.index(org_img_path)
@@ -272,6 +272,21 @@ def run_instance_exp_keras_model(img_path, new_model, feature_extractor_model):
     # st.plotly_chart(plot_n_similar(seed_id,closest_fns, img_path,n=10, scale=4), use_container_width=True)
     st.pyplot(plot_n_similar(seed_id,closest_fns_tmp, img_path,n=10,scale=4))
 
+    with st.expander('Correct Decision'):
+                st.info("Correct th decision if it is not valid. This sample will be added to next training bucket.")
+                tmp_col1, tmp_col2 = st.columns(2)
+                with tmp_col1:
+                    label_correect = st.radio(
+                    "Choose label visibility 👇",
+                    ["CNV", "DME", "NORMAL","DRUSEN"],
+                    horizontal=True)
+                with tmp_col2:
+                    tmp_btn = st.button('ADD TO TRAINING BUCKET')
+                if tmp_btn:
+                    st.warning("Sample added to training set..")
+
+    
+
 def main():
 
     new_model =  get_model(MODEL_PATH)
@@ -340,9 +355,24 @@ def main():
         
         if pressed:
             st.empty()
-            st.sidebar.write('Please wait for the magic to happen! This may take up to a minute.')
+            st.write('Please wait for the magic to happen! This may take up to a minute.')
             run_instance_exp_keras_model(option, new_model,feature_extractor_model)
 
+            # with st.expander('Correct Decision'):
+            #     st.info("Correct th decision if it is not valid. This sample will be added to next training bucket.")
+            #     tmp_col1, tmp_col2 = st.columns(2)
+            #     with tmp_col1:
+            #         label_correect = st.radio(
+            #         "Choose label visibility 👇",
+            #         ["CNV", "DME", "NORMAL","DRUSEN"],
+            #         key="visibility",
+            #         horizontal=True)
+            #     with tmp_col2:
+            #         tmp_btn = st.button('ADD TO TRAINING BUCKET')
+            #     if tmp_btn:
+            #         st.warning("Sample added to training set..")
+
+
 
         # # new_model = load_model(MODEL_PATH)
         # option = st.sidebar.selectbox('Please select a sample image, then click Explain Me button', IMG_PATH_LISTS)